A
Alfredo Canziani
Researcher at New York University
Publications - 11
Citations - 1152
Alfredo Canziani is an academic researcher from New York University. The author has contributed to research in topics: Artificial neural network & Inference. The author has an hindex of 6, co-authored 8 publications receiving 916 citations. Previous affiliations of Alfredo Canziani include Cranfield University & Purdue University.
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An Analysis of Deep Neural Network Models for Practical Applications
TL;DR: This work presents a comprehensive analysis of important metrics in practical applications: accuracy, memory footprint, parameters, operations count, inference time and power consumption and believes it provides a compelling set of information that helps design and engineer efficient DNNs.
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Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic
TL;DR: This work proposes to train a policy by unrolling a learned model of the environment dynamics over multiple time steps while explicitly penalizing two costs: the original cost the policy seeks to optimize, and an uncertainty cost which represents its divergence from the states it is trained on.
Proceedings ArticleDOI
Evaluation of neural network architectures for embedded systems
TL;DR: This work presents a comprehensive analysis of important metrics in practical applications: accuracy, memory footprint, parameters, operations count, inference time and power consumption, and believes it provides a compelling set of information that helps design and engineer efficient DNNs.
Proceedings Article
Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic
TL;DR: In this paper, the authors propose to train a policy by unrolling a learned model of the environment dynamics over multiple time steps while explicitly penalizing two costs: the original cost the policy seeks to optimize, and an uncertainty cost which represents its divergence from the states it is trained on.
Posted Content
CortexNet: a Generic Network Family for Robust Visual Temporal Representations
TL;DR: Inspired by the human visual system, a deep neural network family, CortexNet, is proposed, which features not only bottom-up feed-forward connections, but also it models the abundant top-down feedback and lateral connections, which are present in the authors' visual cortex.